import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				str			,       UpperCamelCase     :				int							)   ->    List[Any]:
		# Initialise PyTorch model
		_lowerCamelCase				   =  AlbertConfig.from_json_file(UpperCamelCase							)
		print(F"""Building PyTorch model from configuration: {config}"""							)
		_lowerCamelCase				   =  AlbertForPreTraining(UpperCamelCase							)

		# Load weights from tf checkpoint
		load_tf_weights_in_albert(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
		torch.save(model.state_dict()			,       UpperCamelCase							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--albert_config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained ALBERT model. \n'
				        'This specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				A						=			parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
A						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
A						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}